Life after trafficking: reintegration experiences of human trafficking survivors in Nigeria
Bibliographic record
Abstract
Purpose Reintegration programs have emerged as part of the regimes of care for survivors of human trafficking. However, empirical analysis of the reintegration outcomes for survivors remains limited in the African context. Hence, this paper aims to examine the challenges and opportunities of reintegration assistance programs for survivors of human trafficking in Nigeria. Design/methodology/approach Drawing on qualitative methods of data collection and analysis, this study conducted semi-structured interviews with repatriated women who have accessed reintegration assistance in Nigeria, and data was analyzed using thematic analysis. Findings The findings suggest that while the reintegration programs might address the procedural aspect of reintegration, the achievement of substantive reintegration remains incomplete. This is because of the structural conditions of the context within which reintegration is supposed to occur. Practical implications There is a need to take seriously the distinction between the reintegration of survivors into a new community or a former community in the design of a regime of care for survivors of human trafficking in Africa. Crucially, the focus on procedural reintegration should not also divert attention away from the structural conditions and reforms needed to ensure survivors achieve substantive reintegration. Originality/value This paper contributes to the limited literature on life after trafficking and demonstrates the strengths and limitations of reintegration programs as a regime of care for survivors of human trafficking. In addition, this study empirically grounded the theoretical distinction between different aspects of the process of reintegration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".